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Title: Sparse tensor discriminative locality alignment for gait recognition
Authors: Zhao, NW
Zhang, L
Du, B
Zhang, LP
Tao, D
You, J 
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of the International Joint Conference on Neural Networks, 2016, v. 2016-October, 7727787, p. 4489-4495 How to cite?
Abstract: Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed for gait data, including the conventional vector representation based methods principal components analysis (PCA) and, locality preserving projection (LPP), and the recently proposed multi-linear subspace learning based approaches such as multilinear principal component analysis (MPCA). In this paper, inspired by the advantages of the tensor representation and manifold learning, we propose a novel sparse tensor discriminative locality alignment for human gait feature representation and dimensionality reduction algorithm, and subsequently apply the refined feature for gait recognition by a lazy classifier of the KNN. The proposed method adopts sparse multi-way projection based on the high-order version of discriminative locality alignment, by which the class separability is enhanced and the potential model overfitting is simultaneously avoided. Extensive experiments on the University of South Florida (USF) HumanID Gait Database show that the proposed method achieves better recognition rate compared with some existing classical dimensionality reduction algorithms.
Description: 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, Canada, 24-29 July 2016
ISBN: 9781509006199
DOI: 10.1109/IJCNN.2016.7727787
Appears in Collections:Conference Paper

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